Review the state-of-the-art technologies of semantic segmentation based on deep learning
Y Mo, Y Wu, X Yang, F Liu, Y Liao - Neurocomputing, 2022 - Elsevier
The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …
information and predict the semantic category of each pixel from a given label set. With the …
A brief survey on semantic segmentation with deep learning
S Hao, Y Zhou, Y Guo - Neurocomputing, 2020 - Elsevier
Semantic segmentation is a challenging task in computer vision. In recent years, the
performance of semantic segmentation has been greatly improved by using deep learning …
performance of semantic segmentation has been greatly improved by using deep learning …
Unext: Mlp-based rapid medical image segmentation network
JMJ Valanarasu, VM Patel - … conference on medical image computing and …, 2022 - Springer
UNet and its latest extensions like TransUNet have been the leading medical image
segmentation methods in recent years. However, these networks cannot be effectively …
segmentation methods in recent years. However, these networks cannot be effectively …
PIDNet: A real-time semantic segmentation network inspired by PID controllers
Two-branch network architecture has shown its efficiency and effectiveness in real-time
semantic segmentation tasks. However, direct fusion of high-resolution details and low …
semantic segmentation tasks. However, direct fusion of high-resolution details and low …
An effective CNN and Transformer complementary network for medical image segmentation
F Yuan, Z Zhang, Z Fang - Pattern Recognition, 2023 - Elsevier
The Transformer network was originally proposed for natural language processing. Due to
its powerful representation ability for long-range dependency, it has been extended for …
its powerful representation ability for long-range dependency, it has been extended for …
UNetFormer: A UNet-like transformer for efficient semantic segmentation of remote sensing urban scene imagery
Semantic segmentation of remotely sensed urban scene images is required in a wide range
of practical applications, such as land cover mapping, urban change detection …
of practical applications, such as land cover mapping, urban change detection …
Deep dual-resolution networks for real-time and accurate semantic segmentation of traffic scenes
Using light-weight architectures or reasoning on low-resolution images, recent methods
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …
realize very fast scene parsing, even running at more than 100 FPS on a single GPU …
Lite-hrnet: A lightweight high-resolution network
We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We
start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution …
start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution …
Efficientvit: Lightweight multi-scale attention for high-resolution dense prediction
High-resolution dense prediction enables many appealing real-world applications, such as
computational photography, autonomous driving, etc. However, the vast computational cost …
computational photography, autonomous driving, etc. However, the vast computational cost …
Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation
Low-level details and high-level semantics are both essential to the semantic segmentation
task. However, to speed up the model inference, current approaches almost always sacrifice …
task. However, to speed up the model inference, current approaches almost always sacrifice …